Objective Anxiety and depression rates are known to be elevated in prematurely-born children and adolescents. This prospective study examines demographic, academic, and physical health correlates of anxiety and depression symptoms in a sample of 10-year-old children who were born extremely preterm. Methods Participants were 889 (51.2% male; 62.3% White) children who were born <28 weeks gestation. Child and family demographic data were collected at birth. When the children were 10, parents (n = 871) and teachers (n = 640) rated the level of anxiety and depression in children through the Child Symptom Inventory-4. Child academic functioning was assessed via the Wechsler Individual Achievement Test-III. Parents completed questionnaires about child academic functioning and physical health issues. Data analyses were conducted with multivariate linear modeling. Results Level of prematurity was significantly related to both parent and teacher reports of anxiety. Public health insurance and individualized education program (IEP) status were associated with both parent and teacher reports of depression. Hispanic ethnicity, public insurance, IEP status, and asthma were significantly associated with both parent-reported anxiety and depression. Gross motor impairment was associated with parent-reported anxiety and teacher-reported depression. Child obesity was associated with teacher reports of anxiety, while male sex was significantly related to teacher reports of depression. Conclusion This pattern of findings may suggest hypotheses for future research on models of the development and persistence of anxiety and depression within this particularly vulnerable group of children.

Artificial intelligence (AI) has emerged as a powerful tool for fostering positive behavior change and enhancing mental health support. However, the abrupt discontinuation or functional degradation of AI-driven interventions, particularly those featuring conversational agents, may trigger unintended psychological consequences. Therefore, we introduce and examine the concept of Artificial Intelligence Discontinuation Effects (AI-DICE), drawing parallels from abandonment-like experiences observed from problematic termination experiences with therapists. We propose a conceptual framework for AI-DICE mitigation that draws on evidence-based behavior change principles and explores clinical modalities that may inform mitigation toolkits (eg, Acceptance and Commitment Therapy, Cognitive Behavioral Therapy, Dialectical Behavior Therapy, and Motivational Interviewing). We also ground our approach in user experience research and community-engaged research. AI-DICE raises critical ethical challenges, including transparency, the ability to withdraw or adapt participation as the users’ knowledge of the intervention grows, and access to support postintervention. Prioritizing long-term continuation, or at least some form of ongoing access, over the best-planned complete discontinuation strategy may help ensure that AI-driven mental health solutions deliver lasting benefits rather than unintended harm. Finally, although not yet empirically established, incorporating discontinuation planning and AI-DICE mitigation from the outset may also improve intervention effectiveness by strengthening user autonomy, supporting skills transfer, and reducing dependence-related vulnerabilities.

UNSTRUCTURED Artificial intelligence (AI) has emerged as a powerful tool for fostering positive behavior change and enhancing mental health support. However, the abrupt discontinuation or functional degradation of AI-driven interventions, particularly those featuring conversational agents, may trigger unintended psychological consequences. Therefore, we introduce and examine the concept of Artificial Intelligence Discontinuation Effects (AI-DICE), drawing parallels from abandonment-like experiences observed from problematic termination experiences with therapists. We propose a conceptual framework for AI-DICE mitigation that draws on evidence-based behavior change principles and explores clinical modalities that may inform mitigation toolkits (eg, Acceptance and Commitment Therapy, Cognitive Behavioral Therapy, Dialectical Behavior Therapy, and Motivational Interviewing). We also ground our approach in user experience research and community-engaged research. AI-DICE raises critical ethical challenges, including transparency, the ability to withdraw or adapt participation as the users’ knowledge of the intervention grows, and access to support postintervention. Prioritizing long-term continuation, or at least some form of ongoing access, over the best-planned complete discontinuation strategy may help ensure that AI-driven mental health solutions deliver lasting benefits rather than unintended harm. Finally, although not yet empirically established, incorporating discontinuation planning and AI-DICE mitigation from the outset may also improve intervention effectiveness by strengthening user autonomy, supporting skills transfer, and reducing dependence-related vulnerabilities.

Background/Objectives: Incorporating parent training into cognitive-behavioral therapy for anxious youth has not been shown to significantly improve outcomes perhaps because these interventions have not addressed potential interfering psychological barriers to implementing parenting changes and rarely offer between-session support. There is growing evidence that Acceptance and Commitment Therapy (ACT) can target these psychological barriers and generate more flexible and adaptive behavioral repertoires in parents of children with a variety of presenting challenges. Methods: Following a pilot trial of “Acceptance and Commitment Therapy for Parents of Anxious Children (ACT-PAC)” a six-week group-based intervention focused on targeting psychological barriers to parenting change using mindfulness and acceptance approaches, we collected qualitative feedback from participants in two post-treatment phases by conducting individual interviews and a focus group with participants that completed the intervention. Results: Analysis of interview responses revealed that parents found ACT principles and processes to be helpful, and many also appreciated the ACT-PAC group setting that allowed parents to recognize their experiences were shared by others and to self-disclose in a non-judgmental space. Feedback from the focus group further provides preliminary evidence that ACT-PAC is acceptable to and feasible for parent participants and suggests modifications such as involving additional caregivers, making resources more readily available, and creative structural changes that may facilitate between-session practice. Conclusions: Results suggest that the group-based intervention can be both maintained and improved for future participants. Limitations to generalizability in light of possible selection bias and the small focus group sample size are addressed.